A new low-rank based framework has been proposed for simultaneous noise attenuation and data reconstruction, which uses an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The online subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analyzing incremental gradient descent on the Grassmannian manifold of subspaces. When the multi-dimensional seismic data is mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the proposed subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional singular value decomposition (SVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed reconstruction method can obtain better performance. More specifically, the proposed method outperforms the singular spectrum analysis (SSA) method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm